Robust algorithm to learn rules for classification: A fault diagnosis case study

Autor:	Balaji Arun P., Sugumaran V.
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	fault diagnosis decision tree modlem rough set machine learning classification Engineering (General). Civil engineering (General) TA1-2040 Mechanics of engineering. Applied mechanics TA349-359
Zdroj:	FME Transactions, Vol 51, Iss 3, Pp 338-346 (2023)
Druh dokumentu:	article
ISSN:	1451-2092 2406-128X
DOI:	10.5937/fme2303338B
Popis:	Machine learning algorithms are used for building classifier models. The rule-based decision tree classifiers are popular ones. However, the performance of the decision tree classifier varies with hyperparameter tuning. The optimum hyperparameter values are obtained using either optimization algorithms or trial and error methods. The present study utilizes the MODLEM algorithm to overcome the drawbacks accounted for by decision tree algorithms. Eliminating hyperparameter tuning and producing results closer to standard decision tree algorithms makes MODLEM a robust classification algorithm. The robustness of the MODLEM algorithm is illustrated with the fault diagnosis case study. The case study is faults diagnosis of an automobile suspension system using vibration signals acquired at various fault conditions.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/b027743271b5448f985bdfba9428fddf Zobrazit plný text záznamu View record in DOAJ